GELTOR: A Graph Embedding Method based on Listwise Learning to Rank

Similarity-based embedding methods have introduced a new perspective on graph embedding by conforming the similarity distribution of latent vectors in the embedding space to that of nodes in the graph; they show significant effectiveness over conventional embedding methods in various machine learning tasks. In this paper, we first point out the three drawbacks of existing similarity-based embedding methods: inaccurate similarity computation, conflicting optimization goal, and impairing in/out-degree distributions. Then, motivated by these drawbacks, we propose AdaSim*, a novel similarity measure for graphs that is conducive to the similarity-based graph embedding. We finally propose GELTOR, an effective embedding method that employs AdaSim* as a node similarity measure and the concept of learning-to-rank in the embedding process. Contrary to existing methods, GELTOR does not learn the similarity scores distribution; instead, for any target node, GELTOR conforms the ranks of its top-t similar nodes in the embedding space to their original ranks based on AdaSim* scores. We conduct extensive experiments with six real-world datasets to evaluate the effectiveness of GELTOR in graph reconstruction, link prediction, and node classification tasks. Our experimental results show that (1) AdaSim* outperforms AdaSim, RWR, and MCT in computing nodes similarity in graphs, (2) our GETLOR outperforms existing state-of-the-arts and conventional embedding methods in most cases of the above machine learning tasks, thereby implying that learning-to-rank is beneficial to graph embedding.